Modulation of Al Distribution in High-Silica ZSM-5 Zeolites for Enhancing Catalytic Performance.

The spatial distribution of framework Al (AlF) has been one of the important factors that affect the catalytic properties of zeolites in diverse chemical reactions; however, the synthesis of high-silica zeolites with special AlF distribution remains a challenge. In this study, we successfully synthesized high-silica ZSM-5 zeolites with a unique AlF distribution by employing pentaerythritol (PET) as an additive in the presence of a few tetrapropylammonium hydroxide (TPAOH). The results demonstrated that the introduction of PET led to a higher proportion of Al atoms located at the sinusoidal and/or straight channels. It was observed that the addition of PET prevented the interaction between TPA+ and tetrahedral [AlO4]- during the crystallization process, resulting in enhanced availability of TPA species in the form of ion-paired TPA+. This effect leads to AlF atoms dominantly distributed away from the intersection and located in narrow channels, where acidic sites more effectively inhibit hydrogen transfer and coke formation. In the reaction of dimethyl ether (DME) to olefins, the catalyst with a unique Al distribution exhibited a significant prolonged catalytic lifetime, surpassing traditional TPA-ZSM-5 by more than 2-fold and maintaining DME conversion above 90% for a maximum of 148 h. The results of multiple pulse experiments also showed that these PET-assisted ZSM-5 zeolites significantly enhanced the selectivity of propene and butene. This approach provides an effective strategy to regulate AlF distribution in high-silica ZSM-5 catalysts with the assistance of neutral alcohol. It holds great potential for application in the synthesis of other high-silica zeolites, thereby enriching the diversity of zeolite catalysis.

GEAR: A database of Genomic Elements Associated with drug Resistance.

Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of Genomic Elements Associated with drug Resistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance. Right now, GEAR contains 1631 associations between 201 human drugs and 758 genes, 106 associations between 29 human drugs and 66 miRNAs, and 44 associations between 17 human drugs and 22 SNPs. These relationships are firstly extracted from primary literature with text mining and then manually curated. The drug resistome deposited in GEAR provides insights into the genetic factors underlying drug resistance. In addition, new indications and potential drug combinations can be identified based on the resistome. The GEAR database can be freely accessed through http://gear.comp-sysbio.org.

PPIM: A Protein-Protein Interaction Database for Maize.

Maize (Zea mays) is one of the most important crops worldwide. To understand the biological processes underlying various traits of the crop (e.g. yield and response to stress), a detailed protein-protein interaction (PPI) network is highly demanded. Unfortunately, there are very few such PPIs available in the literature. Therefore, in this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface. We believe that the PPIM resource can help biologists better understand the maize crop.